Quantum network state modeling and resource scheduling method and system, and readable storage medium

Abstract

The application discloses a kind of quantum network state modeling and resource scheduling method, system and readable storage medium, it is related to quantum communication and quantum network field, the method includes: to quantum network is abstracted, the state parameters of each object are obtained;According to the state parameter, construct the state diagram of network with weight;Response constraint perception and energy level priority routing calculation, determine path screening result;The energy level priority routing calculation includes to candidate path preliminary screening or to candidate path preliminary screening and carry out end-to-end path evaluation;The path screening result includes path preliminary screening result or optimal path decision result;Based on path screening result carries out resource scheduling and path arrangement.The running stability of quantum network, resource utilization and service success rate are improved.

Description

Technical Field

[0001] This invention relates to the fields of quantum communication and quantum network technology, and in particular to a quantum network state modeling and resource scheduling method, system, and readable storage medium. Background Technology

[0002] With the development of quantum communication and quantum information processing technologies, quantum networks are gradually evolving from point-to-point quantum key distribution to a networked system with multiple nodes, cross-regional operations, and multi-service collaboration. The basic resources in a quantum network include entangled pairs, qubits, quantum memories, quantum channels, quantum repeater nodes, quantum gate operation capabilities, and the corresponding classical control links. Compared with classical networks, quantum networks have the following significant characteristics: 1. High fragility of quantum states: Qubits are susceptible to noise, decoherence, and losses; 2. Resources cannot be completely copied: The quantum no-cloning theorem leads to resource sharing and copying methods that differ from classical networks; 3. Significant state evolution over time: Parameters such as fidelity, entanglement, coherence time, and bit error rate are time-varying; 4. High requirements for quantum state quality from services: Different services have different requirements for entanglement purity, fidelity, latency, and success probability; 5. Complex multi-dimensional state coupling: Link loss, node buffering, gate operation error rate, entanglement swap success rate, and service priority are all interconnected.

[0003] Existing technologies have the following shortcomings: 1. They cannot uniformly represent multi-dimensional, heterogeneous, and time-varying network states; 2. There is a lack of unified mapping between quantum state attributes and network resource attributes; 3. They are difficult to support multi-constraint, multi-service adaptive routing scheduling; 4. The computational complexity increases sharply after network expansion. Existing technologies cannot take into account state quality, resource cost, service level, and stability, resulting in poor operational stability, low resource utilization, and low service success rate of quantum networks. Summary of the Invention

[0004] Therefore, the purpose of this invention is to propose a quantum network state modeling and resource scheduling method, system, and readable storage medium to improve the operational stability, resource utilization, and service success rate of quantum networks.

[0005] To achieve the above objectives, the first aspect of this invention proposes a method for quantum network state modeling and resource scheduling, comprising the following steps: Abstract the quantum network into objects and obtain the state parameters of each object; Construct a weighted network state diagram based on the state parameters; The system responds to constraint perception and performs energy-level priority routing calculation to determine the path selection results; the energy-level priority routing calculation includes initial screening of candidate paths or initial screening of candidate paths followed by end-to-end path evaluation; the path selection results include initial path screening results or optimal path decision results. Resource scheduling and path orchestration are performed based on path selection results.

[0006] Based on the first aspect, the quantum network is abstracted into objects, and the state parameters of each object are obtained. According to these state parameters, the multi-dimensional state attributes of nodes, links, and quantum resources are uniformly encoded into multi-dimensional feature vectors. Heterogeneous attributes are transformed into a comparable "service level" space through nonlinear mapping, forming a unified quantitative expression of the network state. A weighted state graph is constructed based on the level mapping results, and a level-first multi-constraint routing algorithm is designed to achieve hierarchical processing of hard and soft constraints, obtaining path selection results. Resource scheduling and path orchestration are then performed based on these results. This significantly improves the operational stability, resource utilization, and service success rate of the quantum network.

[0007] In one possible implementation, the quantum network is abstracted into objects, and the state parameters of each object are obtained, including: Object abstraction is performed on nodes, links, quantum storage units, entanglement resource pools, switching operation units, and classical control links in quantum networks; The state parameters of each object are collected in real time or periodically; the state parameters include at least one of quantum state fidelity, entanglement degree, bit error rate, decoherence time, remaining storage capacity, load rate, link latency, operation success rate and environmental noise level.

[0008] Based on this possible implementation method, each object in the quantum network is abstracted to realize a representation profile of each object, which facilitates the display of state parameters, and the state parameters of each object are collected in real time or periodically, thereby improving the comprehensiveness and accuracy of data acquisition.

[0009] In one possible implementation, constructing a weighted network state graph based on the state parameters includes: Constructing multidimensional state vectors based on state parameters; The state attribute energy level is mapped to the multidimensional state vector according to the preset attribute mapping rules; Construct a weighted network state diagram based on the energy level mapping results of the state attributes.

[0010] In this possible implementation, a multidimensional state vector is constructed based on state parameters; the state attribute energy level is mapped to the multidimensional state vector according to the preset attribute mapping rules; this allows the dimensions that cannot be directly involved in the calculation (such as fidelity, delay, and success probability) to be mapped to the same normalized interval, thereby transforming the multi-constraint routing problem into a reachable path search problem on a weighted graph, which makes it easy to construct a weighted network state graph that meets the user's requirements.

[0011] In one possible implementation, the constraint awareness employs a hierarchical processing mechanism of hard constraints and soft constraints; wherein the hard constraints are used to eliminate paths that do not meet the conditions; and the soft constraints are used to allow the path cost function to be included in the form of a penalty term.

[0012] Based on this possible implementation method, a hierarchical processing mechanism of hard and soft constraints is used to achieve reasonable configuration of constraints. Paths that do not meet the hard constraints are proposed, while soft constraints are allowed to be included in the path cost function in the form of penalty terms for path analysis and screening.

[0013] In one possible implementation, response constraint awareness and energy level priority routing calculation determine the path selection results, including: Initialize the set of constraint conditions corresponding to constraint awareness based on business requirements. An improved K-shortest path algorithm or hierarchical graph search is used to generate a set of candidate paths; Calculate the cumulative energy level value of each path in the candidate path set and remove paths that do not meet the constraints; For the remaining paths, a weighted scoring function is used for initial screening to obtain the initial screening results.

[0014] This possible implementation method facilitates obtaining accurate initial path screening results.

[0015] In one possible implementation, the energy level priority routing calculation further includes: The optimal path decision is determined by comprehensively scoring the end-to-end path based on the initial path screening results and the multi-attribute joint accumulation model. The multi-attribute joint accumulation model includes a fidelity decay model that considers entanglement swapping corresponding to path accumulation fidelity, a linear summation model that corresponds to time delay accumulation, and a weighted summation model that corresponds to resource consumption.

[0016] Based on this possible implementation method, the comprehensive energy level score of the end-to-end path is determined by combining the initial path screening results with the multi-attribute joint cumulative model, thereby determining the optimal path decision result and facilitating further improvement in the accuracy of resource scheduling.

[0017] In one possible implementation, after resource scheduling and path orchestration, the following is also included: Get feedback on actual measurement results, success rate, retransmission count, state degradation, and link changes during business execution; Update online based on feedback.

[0018] Based on this possible implementation method, the technical process of achieving unified quantification of network state, intelligent path optimization, and efficient resource orchestration enables the modeling advantages to be quantitatively transmitted to routing decisions through energy level mapping. The routing decision results are accurately implemented to resource allocation through hierarchical scheduling, and the scheduling execution effect is fed back to the model parameters in real time through a feedback mechanism.

[0019] To achieve the objectives of this invention, a second aspect of this invention proposes a quantum network state modeling and resource scheduling system, the system comprising: The state acquisition module is used to abstract objects in the quantum network and obtain the state parameters of each object. A state diagram generation module is used to construct a weighted network state diagram based on the state parameters. The routing calculation module is used to respond to constraint perception and energy-level priority routing calculation to determine the path selection result; the energy-level priority routing calculation includes preliminary screening of candidate paths or preliminary screening of candidate paths and evaluation of end-to-end paths; the path selection result includes preliminary path screening result or optimal path decision result; The resource scheduling module is used for resource scheduling and path orchestration based on path filtering results.

[0020] The state acquisition module abstracts the quantum network into objects, acquiring the state parameters of each object. The state graph generation module encodes the multi-dimensional state attributes of nodes, links, and quantum resources into multi-dimensional feature vectors based on these state parameters. Through nonlinear mapping, heterogeneous attributes are transformed into a comparable "service level" space, forming a unified quantitative expression of the network state. A weighted state graph is constructed based on the level mapping results. The routing calculation module designs a level-priority multi-constraint routing algorithm, implementing hierarchical processing of hard and soft constraints to obtain path selection results. The resource scheduling module performs resource scheduling and path orchestration based on the path selection results. This significantly improves the operational stability, resource utilization, and service success rate of the quantum network.

[0021] Thirdly, the present invention provides an electronic device, including a memory and one or more processors; the memory is coupled to the processors; the memory stores computer program code, the computer program code including computer instructions, which, when executed by the processor, cause the electronic device to perform the quantum network state modeling and resource scheduling method as described in the first aspect and any of its implementations.

[0022] Fourthly, the present invention provides a computer-readable storage medium including computer instructions that, when executed on an electronic device, cause the electronic device to perform the quantum network state modeling and resource scheduling method as described in the first aspect and any of its implementations.

[0023] The beneficial effects that any of the design schemes in the third to fourth aspects mentioned above can be achieved by referring to the beneficial effects that can be achieved by the first aspect and any of its implementation methods, or by referring to the beneficial effects that can be achieved by the second aspect and any of its implementation methods, and will not be elaborated here.

[0024] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0025] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0026] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart of a quantum network state modeling and resource scheduling method according to an embodiment of the present invention; Figure 2 This is a flowchart of resource scheduling and path orchestration based on path filtering results according to an embodiment of the present invention; Figure 3 This is a flowchart of obtaining the state parameters of each object according to an embodiment of the present invention; Figure 4 This is a flowchart of constructing a weighted network state graph according to an embodiment of the present invention; Figure 5 This is a flowchart illustrating the path filtering results according to an embodiment of the present invention; Figure 6 This is a flowchart of a quantum network state modeling and resource scheduling method according to yet another embodiment of the present invention; Figure 7 This is a flowchart of a quantum network state modeling and resource scheduling method according to yet another embodiment of the present invention; Figure 8 This is a flowchart of an energy level-first multi-constraint routing algorithm according to an embodiment of the present invention; Figure 9 This is a block diagram of a quantum network state modeling and resource scheduling system according to an embodiment of the present invention; Figure 10 This is a block diagram of a quantum network state modeling and resource scheduling system according to yet another embodiment of the present invention; Figure 11 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0027] The technical solutions of the embodiments of this application will be described below with reference to the accompanying drawings. In the description of this application, unless otherwise stated, " / " indicates that the objects before and after are in an "or" relationship; for example, A / B can represent A or B. "And / or" in this application merely describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone, where A and B can be singular or plural. Furthermore, in the description of this application, unless otherwise stated, "multiple" refers to two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. Additionally, to facilitate a clear description of the technical solutions of the embodiments of this application, the terms "first," "second," etc., are used in the embodiments of this application to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first," "second," etc., do not limit the quantity or order of execution, and that "first," "second," etc., are not necessarily different. Furthermore, in the embodiments of this application, words such as "exemplary" or "for example" are used to indicate that something is being used as an example, illustration, or description. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner for ease of understanding.

[0028] Furthermore, the business scenarios described in the embodiments of this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided in the embodiments of this application. As those skilled in the art will know, with the emergence of new business scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0029] For ease of understanding, the technical terms used in the embodiments of this application will be introduced below.

[0030] Nodes in a quantum network are the fundamental physical / logical networking units. They integrate core capabilities such as quantum signal transmission and reception, quantum entanglement preparation, quantum information processing, quantum storage, and quantum switching. They serve as the basic carriers for quantum information generation, transmission relay, and service implementation, and are divided into three categories: terminal nodes, relay nodes, and routing / switching nodes.

[0031] A link in a quantum network is a physical channel between two adjacent quantum nodes, specifically used for transmitting qubits and distributing quantum entangled states.

[0032] Quantum storage units are dedicated functional units deployed within network nodes to cache qubits for short periods and maintain quantum entangled states. They compensate for the timing difference between quantum signal transmission and manipulation, delay quantum state decoherence, solve the time matching problem in quantum entanglement distribution and quantum exchange, and achieve controllable residence of quantum information.

[0033] An entangled resource pool is a collection of pre-prepared, centrally managed, and on-demand entangled quantum state resources in a quantum network.

[0034] The switching operation unit is a dedicated hardware and logic unit in the node responsible for performing quantum entanglement switching, quantum state routing and forwarding, and quantum bit reconstruction. It is used to splice multiple short-distance quantum entangled links into long-distance entangled links to realize cross-node routing of quantum information. It is the core execution module for quantum relay networking and quantum path scheduling.

[0035] The classical control link is an essential auxiliary classical communication channel for quantum networks. Since quantum states themselves cannot transmit control commands, measurement results, network signaling, scheduling configurations, or other information, this link relies on traditional optical fiber or wireless communication to transmit classical control data, enabling global coordination and control of all objects.

[0036] Quantum state fidelity quantifies the similarity between a quantum state after actual transmission, storage, or manipulation and an ideal standard quantum state, with a value ranging from 0 to 1. The closer the value is to 1, the smaller the quantum state distortion and loss, and the better the signal quality. It is a core indicator for evaluating the performance of quantum links and quantum storage.

[0037] Entanglement degree is a physical quantity that describes the strength and purity of quantum entanglement between two or more qubits. The higher the entanglement degree, the stronger the security of quantum communication and the higher the transmission efficiency of quantum repeaters. However, environmental noise will continuously cause the entanglement degree to decay.

[0038] Bit error rate (BER) is the probability that a quantum bit will experience errors such as quantum state flipping or phase shift during transmission, modulation, and readout. It reflects the transmission reliability of the quantum link and node hardware.

[0039] Decoherence time is the longest duration for which a qubit or quantum system can maintain its unique quantum properties, such as quantum superposition and quantum entanglement, without being disrupted by environmental disturbances or collapsing into a normal classical state. A longer decoherence time provides a larger time window for quantum storage, quantum computation, and entanglement operations, making it a key parameter for quantum hardware.

[0040] Remaining storage capacity refers to the amount of idle resources currently available in a quantum storage unit for storing new qubits and entangled states, representing the remaining schedulable carrying capacity of the memory.

[0041] Load rate, which is the ratio of the current occupied resources of entities such as nodes, quantum storage units, and switching operation units to the rated total resource capacity, is used to reflect the busyness of the equipment. Excessive load will lead to increased operation latency and decreased success rate, and is the basis for network load balancing scheduling.

[0042] Link latency is the total time it takes for a quantum bit to be transmitted from one node to another via a quantum link. It includes photon transmission latency and quantum modulation and demodulation processing latency, and measures the real-time transmission performance of a quantum link.

[0043] Operation success rate is the proportion of successful executions of various core operations (entanglement preparation, entanglement swapping, quantum state reading and writing, relay forwarding, etc.) in a quantum network out of the total number of attempts. It reflects the hardware stability, environmental adaptability, and algorithm control precision.

[0044] The environmental noise level is the total intensity of external interference acting on quantum nodes, quantum links, and quantum storage. It includes thermal noise, electromagnetic radiation, mechanical vibration, fiber dispersion scattering, free-space atmospheric turbulence, etc. It is the core cause of decreased fidelity, entanglement decay, accelerated decoherence, and increased bit error rate.

[0045] In quantum network applications, multiple application scenarios are involved, including the basic networking scenario of multi-node cross-regional quantum key distribution, the practical scenario of quantum relay node entanglement exchange and long-distance link splicing, the practical scenario of dynamic caching and network-wide scheduling of quantum storage unit entanglement resource pool, and the practical scenario of multi-service hybrid collaborative quantum network operation. In these application scenarios, there are technical problems such as poor operational stability of quantum networks, low resource utilization, and low service success rate.

[0046] like Figure 1 As shown, the first aspect of this invention proposes a method for quantum network state modeling and resource scheduling, the method comprising steps S1-S4: S1. Abstract the quantum network into objects and obtain the state parameters of each object; In electronic devices running applications that require continuous object abstraction of quantum networks, the electronic device can acquire an abstract instruction stream initiated by the application. This abstract instruction stream instructs the real-time abstraction of multiple objects. The abstract instruction stream includes multiple abstract instructions, each corresponding to an object. The state parameters of each object are acquired and integrated to achieve state acquisition of each object, facilitating subsequent characterization of multidimensional, heterogeneous, and time-varying network states.

[0047] S2. Construct a weighted network state diagram based on the state parameters; Based on the state parameters of each object, the weighted network state graph is used to characterize multidimensional, heterogeneous, and time-varying network states through state parameter processing. Since the state parameters involve multiple objects, the constructed weighted network state graph can display a comprehensive and accurate network state, covering multiple application scenarios and improving scenario adaptability. The weighted network state graph is essentially a weighted graph from classical graph theory, used for routing optimization in quantum networks. The weights are calculated from the fusion value of the service quality levels of multiple attributes.

[0048] S3. Response constraint perception and energy-level priority routing calculation to determine the path selection result; the energy-level priority routing calculation includes preliminary screening of candidate paths or preliminary screening of candidate paths and evaluation of end-to-end paths; the path selection result includes preliminary path screening result or optimal path decision result. This step involves selecting the optimal or suboptimal path from candidate paths based on the service level, quantum state quality requirements, latency requirements, and success rate requirements of the business request. This is achieved using multi-objective optimization, hierarchical screening, heuristic search, dynamic programming, reinforcement learning, or graph search algorithms. During the path calculation process, the accumulation of quantum state degradation, the end-to-end fidelity constraints of entanglement-swapping links, and the dynamic occupancy of node resources are all considered simultaneously.

[0049] S4. Perform resource scheduling and path orchestration based on path selection results.

[0050] like Figure 2 As shown, resource scheduling and path orchestration based on path filtering results include steps S41-S42: S41. Set the resource scheduling objective function; ,in, For transmission delay, For resource consumption costs, The quantum state mass degradation quantity, These are the weighting coefficients.

[0051] S42. Based on the initial path screening results or the optimal path decision results, resources are jointly scheduled using a hierarchical scheduling strategy to determine the resource allocation scheme, execution sequence and reconfiguration actions, and path orchestration is performed according to the determined results; the joint scheduling supports static pre-allocation, dynamic on-demand allocation and prediction-based proactive allocation modes.

[0052] The resources include quantum storage resources, entanglement generation resources, entanglement swapping resources, channel time slot resources, gate operation resources, and classical signaling resources.

[0053] Resource scheduling adopts a hierarchical scheduling strategy: The first layer is strategic layer scheduling: based on long-term traffic prediction, basic entanglement resources and storage resources are pre-allocated; The second layer is the tactical layer scheduling: dynamically allocating available resources and optimizing resource layout based on real-time business requests; The third layer is the execution layer scheduling: adjusting the gate operation sequence and channel time slot allocation in real time, and handling burst degradation and resource conflicts.

[0054] Resource scheduling and path orchestration are performed based on path selection results. The accuracy of resource scheduling is improved through a hierarchical scheduling strategy. However, the quantum network suffers from poor operational stability, low resource utilization, and low service success rate.

[0055] In one possible implementation, such as Figure 3 As shown, the quantum network is abstracted into objects to obtain the state parameters of each object, including steps S11-S12: S11. Object abstraction is performed on nodes, links, quantum storage units, entanglement resource pools, switching operation units, and classical control links in quantum networks; By abstracting each object in the quantum network, a representational profile of each object can be realized, which facilitates the display of state parameters.

[0056] S12. Collect the state parameters of each object in real time or periodically; the state parameters include at least one of quantum state fidelity, entanglement degree, bit error rate, decoherence time, remaining storage capacity, load rate, link latency, operation success rate and environmental noise level.

[0057] By collecting the status parameters of each object in real time, the network status can be updated in a timely manner; by collecting the status parameters of each object periodically, the frequency of network status updates and resource consumption can be taken into account to achieve balanced processing.

[0058] In one possible implementation, the weighted network state graph is G=(V,E,W), where V represents the set of quantum nodes, E represents the set of quantum links and classical control links, and W is the set of weights. The node weights, edge weights, or super-edge weights in the weighted network state graph are calculated by a multi-attribute energy level fusion function, which supports various calculation methods such as weighted summation, fuzzy logic, multi-attribute decision-making, and machine learning.

[0059] For example, the attribute level fusion function uses a weighted summation method: ,in This represents the weight coefficient of the i-th attribute, satisfying... , Let be the energy level value of the i-th attribute.

[0060] Alternatively, the attribute level fusion function employs fuzzy logic fusion: defining the membership function. The energy level values ​​are mapped to a fuzzy set of {excellent, good, medium, poor}, and the comprehensive weight is calculated through fuzzy inference rules. Alternatively, the attribute energy level fusion function employs machine learning-based fusion: a neural network model is trained using historical data, with a multi-dimensional energy level vector as input and a path quality score as output.

[0061] In one possible implementation, such as Figure 4 As shown, the weighted network state diagram is constructed based on the state parameters, including steps S21-S23: S21. Construct a multidimensional state vector based on state parameters; First, based on the state parameters of each object, node state, link state, and business requirement state are constructed to form a node state vector. Link state vector Business state vector wait.

[0062] Node state vector:

[0063]

[0064] in, To preserve the fidelity of quantum states, For entanglement degree, To decoherent time, For the remaining storage capacity, For node load rate, Error rate of door operation; Link state vector: ,in, Link loss rate, For transmission delay, For channel bandwidth, For environmental noise levels, To increase the success rate of entanglement generation; Business state vector: ,in, To request the fidelity threshold, For request rate, To the maximum tolerable delay, For business priority, For safety level requirements.

[0065] Multidimensional state vector Multidimensional state vectors are used to characterize the overall or local state of a network at a given moment. For example, a multidimensional state vector can include at least two of the following: node state vectors, link state vectors, and service state vectors. The required multidimensional state vectors are constructed according to user needs, avoiding the construction of redundant multidimensional state vectors, which increases computational complexity and wastes computational resources. Furthermore, multidimensional state vectors support dynamic dimensional expansion and multi-granularity aggregation; for example, they can expand to include quantum resource states, environmental perturbation states, etc.

[0066] S22. Map the state attribute energy levels of the multidimensional state vector according to the preset attribute mapping rules; According to the preset attribute mapping rules, quantum state attributes and network resource attributes such as fidelity, entanglement, bit error rate, decoherence time, success probability, latency, remaining buffer, load rate, and operation error rate are mapped to discrete or continuous attribute energy level values, forming a unified energy level expression that can be sorted, clustered, and compared. The attribute mapping rules include linear mapping, nonlinear mapping, piecewise mapping, and adaptive mapping based on service quality level.

[0067] The core of state attribute energy level mapping lies in solving the fundamental problem of "incomparable heterogeneous parameters" in quantum network routing decisions: fidelity is [0,1], latency is [0,∞) time quantity, success probability is probability value, and load rate is resource proportion. These four have different dimensions and different value ranges, making it impossible to directly perform algorithmic calculations or compare their sizes. By mapping each attribute to a unified [0,1] energy level space, three key objectives are achieved: (1) Dimensionlessness - eliminating dimensional differences and making different attributes computable; (2) Monotonic order preservation - maintaining the superiority-inferiority partial order relationship of the original attributes to ensure that the mapping does not distort the decision logic; (3) Controllable discrimination - adjusting the discrimination sensitivity of different intervals through mapping parameters (such as α, β, N) to enable the decision system to have higher resolution for key quality intervals (such as high fidelity and low latency).

[0068] The specific implementation of state attribute energy level mapping includes: Regarding fidelity Using a nonlinear mapping function ,in The compression factor is used to expand the high-fidelity range to distinguish high-quality resources. Adjustable independently For latency Piecewise mapping is used: when hour, ; hour, ,in, For the time delay threshold, The attenuation coefficient is used; the output range of the above mapping is... ,when The energy level reaches a maximum of 1 (optimal) and decays exponentially as D increases, ensuring that the energy level of high-latency paths is significantly suppressed and thus preferentially eliminated in route selection; the energy level mapping of all attributes is normalized to the [0,1] interval to ensure that different attributes have the same numerical scale and comparability in fusion calculation.

[0069] For path loss Using mapping ; For success rates P∈[0,1], a power function nonlinear mapping function is used: ,in, This is the success rate compression factor; Adjustable independently; Load rate Load∈[0,1], using the inverse linear mapping function: E Load =1-Load; For discrete attributes, an adaptive mapping based on business level is used to divide the attribute values ​​into... There are three energy level ranges, each corresponding to a specific service quality level.

[0070] The aforementioned energy level mapping establishes a quantitative transformation relationship from the original physical parameters to a unified decision space. This allows dimensions that were originally not directly involved in the calculations (such as fidelity, latency, and success probability) to be mapped to the same normalized interval, thereby transforming the multi-constraint routing problem into a reachable path search problem on a weighted graph. By maintaining the partial order of attributes through monotonic mapping, it ensures that the magnitude of the mapped energy level values ​​is consistent with the relative merits of the original attributes, providing an interpretable and comparable quantitative basis for subsequent routing decisions.

[0071] S23. Construct a weighted network state diagram based on the state attribute energy level mapping results.

[0072] The state attribute energy level mapping results are parameters in the same normalized interval, and a weighted network state diagram corresponding to the user's needs can be constructed.

[0073] In one possible implementation, such as Figure 5 As shown, the response constraint perception and energy level priority routing calculation determine the path selection results, including steps S31-S34: S31. Initialize the set of constraint conditions corresponding to constraint awareness according to business requirements. Business requirements include service level, quantum state quality requirements, latency requirements, and success rate requirements for business requests.

[0074] Determine the set of constraints Including minimum fidelity Maximum delay Minimum success rate This facilitates the application of hard and soft constraints based on a set of constraint conditions.

[0075] S32. Use an improved K-shortest path algorithm or hierarchical graph search to generate a candidate path set; A candidate path set is generated using an improved K-shortest path algorithm, specifically: the improved K-shortest path algorithm. This refers to a candidate path generation method that introduces energy level pre-filtering and hop count constraints into the traditional KSP framework. S321. Search Space Pre-Pruning: Before running KSP, unusable nodes and links are removed using hard constraints. For example, nodes with fidelity lower than [a certain value] are removed. The link and latency exceed The nodes are removed directly from the graph, allowing KSP to run on the pruned subgraph, reducing the search space by 30%-70%.

[0076] S322. Multi-dimensional edge weights replace single edge weights: Traditional KSP sorts paths using single edge weights (such as distance), while the improved version uses energy level fusion weights. As edge weights, the concept of "shortest" in KSP is expanded from a single dimension to a comprehensive optimal solution based on multiple attributes.

[0077] S323, Hop Count Constraint: Set the maximum hop count When the path hop count reaches The expansion of this branch is forcibly terminated at that time. This is because the fidelity of entanglement swapping in quantum networks is determined by... Exponential decay means that paths exceeding a certain number of hops become practically unusable, regardless of how excellent their other attributes are.

[0078] S324. Dynamically Determined K Value: K is not a fixed constant, but is dynamically adjusted according to network size and business needs. In lightly loaded networks, a value of 3-5 is sufficient to cover high-quality paths; in highly dynamic networks, a value of 10-20 is used to ensure the diversity of the candidate set. The upper bound of K is determined by the computing resource budget.

[0079] A hierarchical graph search is employed to generate a candidate path set. Specifically, the core idea of ​​hierarchical graph search is to progressively reduce the search space through multi-level graph abstraction, making it suitable for large-scale quantum networks. The specific technical solution is as follows: (1) Hierarchical graph construction: Intra-domain Layer: The quantum network is divided into multiple domains according to physical topology or management boundaries, and each domain retains complete node and link information; Inter-domain Layer: Each domain is abstracted as a super node, and inter-domain links are abstracted as super edges to construct a high-level topology graph; Global Layer: An optional higher-level abstraction used for cross-regional path planning; (2) Candidate path generation process: Step 1: Inter-domain path search. Run the shortest path algorithm (such as Dijkstra) at the inter-domain layer to determine the sequence of domains through which the business flow passes. Step 2: Intra-domain path concatenation. For each intra-domain path segment in the inter-domain path, run the KSP algorithm independently on the corresponding intra-domain layer graph to generate local candidate paths. Step 3: End-to-end path combination, which involves piecing together the intra-domain paths of each segment in the order of inter-domain paths to form a complete set of end-to-end candidate paths; Step 4: Constraint filtering, applying hard constraints (minimum fidelity F) min Maximum delay D max Remove paths that do not meet the conditions.

[0080] S33. Calculate the cumulative energy level value of each path in the candidate path set and remove paths that do not meet the constraints. The specific steps are as follows: First, calculate the end-to-end cumulative value of each attribute: For each path p in the candidate path set, calculate each attribute (fidelity is expressed using F). end-to-end Calculation and delay use D end-to-end The calculation and resource consumption are performed using R. end-to-end First, calculate the end-to-end cumulative value; second, map the cumulative energy level value to the energy level value using the mapping function in step S22; finally, perform hard constraint screening: if F end-to-end <F min : Remove paths; if D end-to-end >D max : Path removal; facilitates accurate and rapid removal of paths that do not meet the constraints, enabling fast filtering.

[0081] S34. For the remaining paths, a weighted scoring function is used for initial screening to obtain the initial screening results.

[0082] Using a weighted scoring function An initial screening is conducted, and the path with the highest score is selected for end-to-end evaluation.

[0083] in, , and These are the weighting coefficients for the corresponding items; Accumulate the path fidelity energy level value. Through a nonlinear mapping function, fidelity is the primary constraint metric for routing in quantum networks. The actual physical significance of the high-fidelity range (such as 0.9-1.0) is far greater than that of the low-fidelity range. Through power compression, the mapped energy level values ​​have higher resolution in the high-fidelity range, enabling high-quality resources to gain a more significant competitive advantage in routing ranking. The cumulative latency level value for the path is obtained through piecewise exponential mapping. The latency has a clear threshold effect—within the business-tolerable latency range (D≤D). th The path is equivalent and optimal; after exceeding the threshold, the damage to the business experience is amplified exponentially for each unit increase in latency; segmented mapping accurately describes this physical characteristic; This represents the resource consumption energy level value, which is determined by the total resource consumption along the path. Obtained through normalized mapping. Total resource consumption of the path. ,in This represents the resource consumption of the k-th node (the sum of quantum storage usage and gate operation overhead). This represents the bandwidth usage of the i-th link segment. The resource consumption level mapping is defined as: when... hour, ;when hour, (At this point, the resource limit is exceeded, triggering soft-constraint multiplication.) This mapping converts resource consumption into a [0,1] energy level space. The closer to 1, the less resources are used (the better).

[0084] The remaining paths are initially screened based on a weighted scoring function to obtain accurate initial path screening results.

[0085] In one possible implementation, the energy level priority routing calculation further includes: The optimal path decision is determined by comprehensively scoring the end-to-end path based on the initial path screening results and the multi-attribute joint accumulation model. The multi-attribute joint accumulation model includes a fidelity decay model that considers entanglement swapping corresponding to path accumulation fidelity, a linear summation model that corresponds to time delay accumulation, and a weighted summation model that corresponds to resource consumption.

[0086] The comprehensive energy level score for end-to-end paths adopts a multi-attribute joint cumulative model, denoted as . Local link scoring Used for initial screening of candidate paths Used for final path selection.

[0087] Path accumulation fidelity calculation takes into account fidelity decay due to entanglement swaps: ,in Let i be the fidelity of the i-th link segment. To ensure the fidelity of entanglement swap operations, This represents the link hop count.

[0088] The delay accumulation uses a linear summation model: ,in, This represents the number of hops in the path. Let be the delay of the i-th link segment. The entanglement exchange processing delay for the j-th intermediate relay node on path p includes the Bell measurement operation delay, classical channel communication delay, and quantum state preparation delay; Resource consumption is modeled using a weighted sum: ,in, , where is the number of intermediate nodes in the path. Let be the resource consumption of the kth intermediate node. This represents the bandwidth usage of the i-th link segment; The end-to-end integrated path score combines the cumulative values ​​of fidelity, latency, and resources by mapping their respective energy levels and then weighting and fusing them together. This enables an upgrade in decision-making from single-dimensional fidelity constraints to multi-dimensional comprehensive optimality.

[0089] and The difference is: Based on the rapid evaluation of single-hop local energy level values, the computational complexity is low, making it suitable for large-scale initial screening of selection paths. Based on the energy level mapping of end-to-end cumulative values, considering the full-link effects of entanglement swap fidelity decay, latency accumulation, and resource superposition, it is suitable for final path decision.

[0090] In calculation and At that time, the weighting coefficients , , Consistent.

[0091] Based on the initial path screening results and the multi-attribute joint cumulative model, the comprehensive energy level score of the end-to-end path is determined to identify the optimal path decision, which facilitates further improvement in the accuracy of resource scheduling.

[0092] In one possible implementation, the constraint awareness employs a hierarchical processing mechanism of hard and soft constraints. For example, minimum fidelity... and maximum latency This is a hard constraint; any path that does not meet the hard constraint will be directly eliminated to ensure the minimum service quality requirements and minimum success rate of the business. and resource consumption As a soft constraint, it allows the path cost function to be included in the form of a penalty term.

[0093] Penalty items are constructed based on the attribute optimization direction: for attributes such as success rate, where "the higher the better," the penalty value is... ,in, As the success rate threshold, This represents the actual end-to-end success rate of the path. There was no punishment at the time. The penalty is applied linearly based on the time difference; for attributes such as resource consumption where "the smaller the better," the penalty value is... ,in, This is the upper limit threshold for resource consumption. This represents the actual resource consumption of the path. This represents the resource consumption of the k-th node (the sum of quantum storage usage and gate operation overhead). This represents the bandwidth usage of the i-th link segment. When There was no punishment at the time. Apply an over-limit linear penalty. and The penalty coefficients for the corresponding attributes are used to adjust the weight of soft constraints in the total path cost.

[0094] like Figure 6 As shown, in one possible implementation, after resource scheduling and path orchestration, steps S5-S6 are also included: S5. Obtain and provide feedback on actual measurement results, success rate, retransmission count, state degradation, and link changes during the business execution process; The dynamic feedback mechanism supports two modes: centralized network controller updates and distributed node self-updates.

[0095] Implement a dynamic feedback mechanism, including: Real-time monitoring: Obtain the actual fidelity of the link through quantum tomography or Bell state measurement, and obtain latency and success rate data through classical channels; State update: Kalman filter or particle filter algorithm is used to fuse predicted and measured values ​​to update the state vector parameters; Adaptive adjustment: When the fidelity of a link is detected to be below the threshold or the load of a node exceeds the capacity, rerouting or resource reallocation is triggered; Learning optimization: Utilize reinforcement learning algorithms to optimize mapping rules and weight coefficients based on historical scheduling results.

[0096] S6. Update online based on feedback information.

[0097] Update the state vector, attribute energy level, and path cost to achieve online adaptive optimization.

[0098] like Figure 7 As shown, a quantum network state modeling and resource scheduling method is proposed, including: Network object abstraction and state acquisition; Constructing quantum network state vectors; State vector energy level mapping; Generate a weighted network state diagram; Constraint sensing and energy level priority routing calculation; Layered resource scheduling and path orchestration; Dynamic feedback and online adaptive updates.

[0099] By constructing a multidimensional state vector model for quantum networks, the diverse state attributes of nodes, links, and quantum resources are uniformly encoded into multidimensional feature vectors. Heterogeneous attributes are transformed into a comparable "service energy level" space through nonlinear mapping, forming a unified quantitative expression of network state. Based on the energy level mapping results, a weighted state graph is constructed, and an energy level-first multi-constraint routing algorithm is designed to achieve hierarchical processing of hard and soft constraints. Combined with hierarchical resource scheduling and reinforcement learning-driven online adaptive updates, the operational stability, resource utilization, and service success rate of quantum networks are significantly improved, overcoming the problems of fragmented state expressions, single-dimensional path selection, and insufficient resource scheduling accuracy in existing quantum networks. By establishing a complete technical process of "unified multidimensional state vector representation—attribute energy level mapping—weighted state graph construction—multi-constraint energy level-first routing—hierarchical joint scheduling—online adaptive feedback," a technical process is achieved that enables unified quantification of network state, intelligent path optimization, and efficient resource orchestration. This allows the modeling advantages to be quantitatively transferred to routing decisions through energy level mapping, the routing decision results to be accurately implemented in resource allocation through hierarchical scheduling, and the scheduling execution effect to be fed back to the model parameters in real time through a feedback mechanism.

[0100] like Figure 8 As shown, an energy-level priority multi-constraint routing algorithm is proposed, including: Business request input; Constraint initialization; Candidate path generation; Path energy level screening; Determine if the constraints are met; if the constraints are met, eliminate the paths; if the constraints are met, perform a weighted score, select the optimal path, and output the path results.

[0101] The energy-level priority multi-constraint routing algorithm simultaneously considers quantum state degradation accumulation, end-to-end fidelity constraints of entangled switching links, and dynamic occupancy of node resources during path calculation, thus accurately outputting path results.

[0102] like Figure 9 As shown, to achieve the above objectives, a second aspect of the present invention proposes a quantum network state modeling and resource scheduling system, the system comprising: State acquisition module 1 is used to abstract objects in the quantum network and obtain the state parameters of each object; The state acquisition module includes a quantum measurement unit and a classical monitoring unit. The quantum measurement unit acquires quantum state parameters through quantum tomography, Bell state test, or entanglement witness measurement; the classical monitoring unit acquires classical network parameters such as link latency and load rate through standard network device management protocol SNMP, traffic logging technology NetFlow, or a custom protocol.

[0103] State diagram generation module 2 is used to construct a weighted network state diagram based on the state parameters; The routing calculation module 3 is used to respond to constraint perception and energy-level priority routing calculation to determine the path selection result; the energy-level priority routing calculation includes preliminary screening of candidate paths or preliminary screening of candidate paths and evaluation of end-to-end paths; the path selection result includes preliminary path screening result or optimal path decision result; Resource scheduling module 4 is used for resource scheduling and path orchestration based on path filtering results.

[0104] The quantum network state modeling and resource scheduling system supports both centralized control and distributed autonomy operating modes. In centralized mode, the network controller uniformly maintains the global state vector and performs global optimization. In distributed mode, each node maintains its local state vector, and state synchronization and consistency maintenance are achieved through the Gossip protocol or blockchain mechanism. The Gossip protocol (also known as the Epidemic Protocol or Rumor Protocol) is a decentralized peer-to-peer communication protocol whose core mechanism mimics the spread of viruses or gossip in reality, achieving efficient information propagation and eventual consistency in large-scale distributed systems.

[0105] like Figure 10 As shown, a quantum network state modeling and resource scheduling system includes: State acquisition module 1 performs object abstraction of the quantum network and obtains the state parameters of each object; The energy level mapping module 12 is used to encode state parameters into multidimensional state vectors and map attribute values ​​in the multidimensional state vectors into unified attribute energy levels. State diagram generation module 2 is used to construct weighted network state diagrams based on energy level mapping results; The routing calculation module 3 is used to respond to constraint perception and energy-level priority routing calculation to determine the path selection result; the energy-level priority routing calculation includes preliminary screening of candidate paths or preliminary screening of candidate paths and evaluation of end-to-end paths; the path selection result includes preliminary path screening result or optimal path decision result; Resource scheduling module 4 is used for resource scheduling and path orchestration based on path filtering results.

[0106] Feedback update module 5 is used to dynamically update the network state model based on the execution results.

[0107] Based on the above quantum network state modeling and resource scheduling system, the following advantages are achieved: 1. Strong unified modeling capability: It integrates quantum state attributes and network resource attributes into the state vector model, facilitating the description of complex quantum network states. 2. Support for multi-attribute cooperative routing: Through attribute energy level mapping, heterogeneous parameters that are difficult to compare directly are mapped to a unified space, facilitating comprehensive decision-making. 3. Improved routing accuracy and service success rate: It can consider multiple factors such as fidelity, success probability, latency, cache usage, and load to select a better path. 4. Improved resource utilization: Through state-aware scheduling, it achieves fine-grained allocation of quantum storage, entanglement resources, and link resources, reducing resource waste. 5. Adaptability to dynamic environments: It can provide real-time feedback and dynamic reconfiguration for decoherence, link fluctuations, node failures, and sudden service loads. 6. Good scalability: It can be applied to quantum networks of different scales, protocol stacks, and physical implementations.

[0108] Thirdly, embodiments of this application provide an electronic device, including a memory and one or more processors; the memory is coupled to the processors; the memory stores computer program code, which includes computer instructions, and when the computer instructions are executed by the processor, the electronic device performs the quantum network state modeling and resource scheduling method as described in the first aspect and any of its implementations.

[0109] The foregoing primarily describes the solutions provided by the embodiments of this application from a methodological perspective. To achieve the aforementioned functions, it includes corresponding hardware structures and / or software modules for executing each function. Those skilled in the art should readily recognize that, based on the method steps of the examples described in conjunction with the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0110] It is understood that the above description of the quantum network state modeling and resource scheduling method is merely illustrative of method embodiments. These embodiments are not intended to limit the quantum network state modeling and resource scheduling method. In other embodiments, multiple embodiments or parts of multiple embodiments of this application can be combined, and the combined scheme can be implemented. Optionally, some operations in the process of each method embodiment can be arbitrarily combined, and / or the order of some operations can be arbitrarily changed. Furthermore, the execution order between the steps of each process is merely exemplary and does not constitute a limitation on the execution order between steps; other execution orders are also possible. It is not intended to indicate that the execution order is the only possible order in which these operations can be performed. Those skilled in the art will conceive of various ways to reorder the operations described in the embodiments of this application. Additionally, it should be noted that the process details involved in a certain embodiment of this application are similarly applicable to other embodiments, or different embodiments can be combined.

[0111] Furthermore, some steps in the method embodiments can be equivalently replaced with other possible steps. Alternatively, some steps in the method embodiments may be optional and can be deleted in certain use cases. Or, other possible steps may be added to the method embodiments.

[0112] Furthermore, the various method embodiments can be implemented individually or in combination.

[0113] It is understood that, in order to achieve the above functions, the electronic device includes hardware and / or software modules that perform the respective functions. Based on the algorithmic steps of the examples described in conjunction with the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is implemented in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application in conjunction with the embodiments, but such implementation should not be considered beyond the scope of this application.

[0114] This embodiment can divide the electronic device into functional modules according to the above method example. For example, each function can be divided into its own functional modules, or two or more functions can be integrated into one processing module. The integrated modules can be implemented in hardware. It should be noted that the module division in this embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.

[0115] like Figure 11 As shown, Figure 11This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device shown may include one or more processors 1301, memory 1302, and communication interfaces 1303.

[0116] The communication interface 1303 is used for data transmission with other devices. The memory 1302 stores computer program code. The computer program code includes computer instructions, which, when executed by the processor 1301, cause the electronic device to perform the quantum network state modeling and resource scheduling method described in this embodiment.

[0117] The processor 1301 may be a processor or controller, such as a central processing unit (CPU), a general-purpose processor, a field-programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It may implement or execute the various exemplary logic blocks, modules, and circuits described in connection with this disclosure. The processor may also be a combination that implements computational functions, such as including one or more microprocessor combinations, etc.

[0118] Bus 1304 can be a Peripheral Component Interconnect Standard (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. Bus 1304 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 11 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0119] Fourthly, embodiments of this application provide a computer-readable storage medium including computer instructions that, when executed on an electronic device, cause the electronic device to perform the quantum network state modeling and resource scheduling method as described in the first aspect and any of its implementations.

[0120] The electronic device and computer-readable storage medium provided in this application are used to execute the corresponding methods provided above. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects of the corresponding methods provided above, and will not be repeated here.

[0121] Through the above description of the embodiments, those skilled in the art can clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0122] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another apparatus, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0123] The units described as separate components may or may not be physically separate. A component shown as a unit can be one or more physical units; that is, it can be located in one place or distributed in multiple different locations. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0124] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0125] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, or the parts that contribute to the solution, or all or part of the technical solution, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0126] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for state modeling and resource scheduling in quantum networks, characterized in that, Includes the following steps: Abstract the quantum network into objects and obtain the state parameters of each object; Construct a weighted network state diagram based on the state parameters; Based on the weighted network state diagram response constraint perception and energy-level priority routing calculation, the path selection result is determined; the energy-level priority routing calculation includes preliminary screening of candidate paths or preliminary screening of candidate paths and evaluation of end-to-end paths; the path selection result includes preliminary path screening result or optimal path decision result. Resource scheduling and path orchestration are performed based on path selection results; The process of abstracting objects from the quantum network and obtaining the state parameters of each object includes: Object abstraction is performed on nodes, links, quantum storage units, entanglement resource pools, switching operation units, and classical control links in quantum networks; The state parameters of each object are collected in real time or periodically; the state parameters include at least one of quantum state fidelity, entanglement degree, bit error rate, decoherence time, remaining storage capacity, load rate, link latency, operation success rate and environmental noise level; Constructing a weighted network state graph based on the state parameters includes: Constructing multidimensional state vectors based on state parameters; The state attribute energy level is mapped to the multidimensional state vector according to the preset attribute mapping rules; Construct a weighted network state diagram based on the energy level mapping results of the state attributes.

2. The quantum network state modeling and resource scheduling method as described in claim 1, characterized in that, The constraint awareness employs a hierarchical processing mechanism of hard constraints and soft constraints; wherein, the hard constraints are used to eliminate paths that do not meet the conditions; and the soft constraints are used to allow the path cost function to be included in the form of a penalty term.

3. The quantum network state modeling and resource scheduling method as described in claim 1, characterized in that, The response constraint perception and energy level priority routing calculation determine the path selection results, including: Initialize the set of constraint conditions corresponding to constraint awareness based on business requirements. An improved K-shortest path algorithm or hierarchical graph search is used to generate a set of candidate paths; Calculate the cumulative energy level value of each path in the candidate path set and remove paths that do not meet the constraints; For the remaining paths, a weighted scoring function is used for initial screening to obtain the initial screening results.

4. The quantum network state modeling and resource scheduling method as described in claim 3, characterized in that, The energy level priority routing calculation also includes: The optimal path decision is determined by comprehensively scoring the end-to-end path based on the initial path screening results and the multi-attribute joint accumulation model. The multi-attribute joint accumulation model includes a fidelity decay model that considers entanglement swapping corresponding to path accumulation fidelity, a linear summation model that corresponds to time delay accumulation, and a weighted summation model that corresponds to resource consumption.

5. The quantum network state modeling and resource scheduling method as described in any one of claims 1-4, characterized in that, After performing resource scheduling and path orchestration based on the path filtering results, the process also includes: Get feedback on actual measurement results, success rate, retransmission count, state degradation, and link changes during business execution; Update online based on feedback.

6. A quantum network state modeling and resource scheduling system, used to implement the method according to any one of claims 1-5, characterized in that, The system includes: The state acquisition module is used to abstract objects in the quantum network and obtain the state parameters of each object. A state diagram generation module is used to construct a weighted network state diagram based on the state parameters. The routing calculation module is used to respond to constraint perception and energy-level priority routing calculation to determine the path selection result; the energy-level priority routing calculation includes preliminary screening of candidate paths or preliminary screening of candidate paths and evaluation of end-to-end paths; the path selection result includes preliminary path screening result or optimal path decision result; The resource scheduling module is used for resource scheduling and path orchestration based on path filtering results.

7. An electronic device, characterized in that, include: A memory and a processor, the memory for storing a computer program and the processor for executing the computer program to perform the quantum network state modeling and resource scheduling method as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, It stores computer instructions that, when executed on an electronic device, cause the electronic device to perform the quantum network state modeling and resource scheduling method as described in any one of claims 1-5.

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